retellai-performance-tuning

Quick Review

This skill provides comprehensive technical implementations for optimizing Retell AI API performance through caching, batching, and connection pooling. The description adequately covers when to invoke the skill with clear trigger phrases. Task knowledge is strong with detailed TypeScript code examples for LRU caching, Redis distributed caching, DataLoader batching, connection pooling, and pagination. The structure is logical with clear sections and helpful benchmarks/error tables. However, novelty is limited as these are standard performance optimization patterns (caching, batching, connection pooling) that a CLI agent could implement with moderate prompting. The techniques shown are well-established best practices rather than domain-specific complex integrations unique to Retell AI. The skill is useful for consolidating these patterns but doesn't represent highly specialized knowledge that would be difficult for an agent to reproduce.